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Paper Citation Record · LEDGER

Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1611.01722.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1611.01722 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T12:38:14.338402Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-07-02T22:27:25.867136Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation a017ff77-6f1f-454f-b3f9-96bd28d87566 · inbound

DIME:Diffusion-Based Maximum Entropy Reinforcement Learning cites this paper.

DIME:Diffusion-Based Maximum Entropy Reinforcement Learning Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-09T12:38:14.338402Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T12:38:14.338402Z digest=sha256:00ccbca2e10eb1de7010458f9d97ed1d8911aa170cdbd80c379116abd275f07a

Observation cd2a205f-d298-48a9-8c44-cff38ce664eb · inbound

Reinforcement Learning for Flow-Matching Policies with Density Transport cites this paper.

Reinforcement Learning for Flow-Matching Policies with Density Transport Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning

Reference 48

Resolution
verified exact
local_arxiv, observed 2026-07-02T22:27:25.868477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-27T18:55:02.040180Z digest=sha256:0f9209834036a27264535368582c965d5073d890765c2b6074171d297e3e2678